Claude Sonnet 4 vs Claude Sonnet 5
Compare Claude Sonnet 4 and Claude Sonnet 5 side-by-side. See how these vision models stack up in Image Captioning, OCR, Object Detection, Open Prompt, and Classification.
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Claude Sonnet 4 is deprecated and can no longer be run. Details and evals are still available on its model page.
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Claude Sonnet 4 vs Claude Sonnet 5: Overview
Claude 4 Sonnet, released by Anthropic in May 2025, is the mid-tier model in the Claude 4 family, designed to balance capability, cost, and speed. It is multimodal, accepting both text and images, and extends beyond prior versions with improved “computer use” support, allowing API-driven interaction with desktop-like interfaces. By default, it supports 200,000 tokens of context, but as of August 2025, it also offers a 1 million-token context window in public beta—making it one of the most context-capable models available for processing entire codebases or large document sets in a single request.
Sonnet 4 is significantly cheaper than the flagship Opus while still demonstrating strong reasoning, coding, and instruction-following ability with reduced hallucinations. Its extended context capabilities and lower latency make it well-suited for enterprise-scale knowledge management, software development, research assistants, and productivity automation where both cost efficiency and high reliability are essential.
Claude Sonnet 5 is a mid-tier large language model from Anthropic, released on June 30, 2026, as the latest model in the Sonnet series and a direct successor to Claude Sonnet 4.6. It is a hybrid reasoning model designed primarily for agentic workflows, software coding, and professional tasks. The model features a 1 million token context window, a 128k maximum output token limit, and runs adaptive thinking by default, giving API users fine-grained control over reasoning effort across five levels (low, medium, high, max, and extra-high). It uses an updated tokenizer shared with Opus 4.7 and later models, which produces approximately 30% more tokens for equivalent text compared to earlier Claude models. On benchmarks, Sonnet 5 scores 63.2% on agentic coding and 81.2% on OSWorld, narrowing the gap with Opus 4.8 while remaining at Sonnet-tier pricing.
The model supports text and image input with text output, and accepts tools including browsers and terminals for autonomous multi-step task execution. Anthropic's safety evaluations report that Sonnet 5 shows a lower rate of undesirable behaviors than Sonnet 4.6 and is generally safer in agentic contexts, with improved resistance to prompt injection and reduced sycophancy. Cybersecurity safeguards equivalent to those on Opus 4.7 and 4.8 are active, though Anthropic notes the model was not deliberately trained on cybersecurity tasks. The model is proprietary and API-only, with no open weights.
Claude Sonnet 4 vs Claude Sonnet 5 Comparison Table
| Property | Claude Sonnet 4 | Claude Sonnet 5 |
|---|---|---|
| Organization | Anthropic | Anthropic |
| Category | closed | closed |
| Modality | multimodal | multimodal |
| Release Date | May 2025 | Jun 2026 |
| Context Window | 1.0M | 1.0M |
| Parameters | ||
| License | Proprietary | Proprietary |
| Pricing per 1M tokens | ||
| Input $/1M | $3.00 | $2.00 |
| Output $/1M | $15.00 | $10.00 |
| Vision Tasks | ||
| Captioning | Demo | |
| Classification | Demo | |
| Object Detection | Demo | |
| OCR | Demo | |
| Vision Language | ||
| Visual Question Answering | Demo | |
| Document Question Answering | ||
| Multi-Label Classification | ||
| Model Features | ||
| LLMs with Vision Capabilities | ||
| Multimodal Vision | ||
| Foundation Vision | ||
Vision Evalspass/fail results · 67 prompts Score key:≥75%40–74%<40% | ||
| Visual Understanding | ||
| Overall Score | 68.66% | 70.15% |
| Avg Response Time | 21.26s | 3.90s |
| Median input tokensincl. image tokens | 2.1K | |
| Median output tokens | 61 | |
| Est. cost / taskon this benchmark | $0.0048 | |
| Defect Detection | 80%(12/15) | 73.3%(11/15) |
| Document Understanding | 88.9%(8/9) | 66.7%(6/9) |
| Object Counting | 20%(2/10) | 20%(2/10) |
| Object Understanding | 78.6%(11/14) | 92.9%(13/14) |
| Spatial Understanding | 68.4%(13/19) | 78.9%(15/19) |
| OCR | ||
| Overall Score | 83.84% | |
| Avg Response Time | 2.77s | |
| Median input tokensincl. image tokens | 642 | |
| Median output tokens | 64 | |
| Est. cost / taskon this benchmark | $0.0019 | |
| Focused Scene OCR | 88.9%(88/99) | |
| Handwritten Math | 50%(5/10) | |
| License Plate Recognition | 90%(27/30) | |
| Text Recognition | 80%(24/30) | |
| VQA & Extraction | 80%(48/60) | |
Output tokens (incl. reasoning) and est. cost / task are measured on this benchmark from a single low-temperature run, and shown only for models whose run covered at least 90% of prompts. Methodology